Develop a Nonlinear Regression Model Using Box-Cox Transformation with Application


  • Rozheen Taher Awdi Department of Statistics, University of Duhok, Kurdistan Region, Iraq
  • Haithem Taha Mohammed Ali Department of Economic Sciences, University of Zakho, Kurdistan Region, Iraq, and Department of Economics, Nawroz University, Kurdistan Region, Iraq



Multiple linear regression, Box-Cox transformation


This article introduces an algorithm designed for utilizing power transformations in the estimation of nonlinear regression models. The algorithm outlines a series of steps for selecting the most suitable power parameter estimate through a combination of the conventional Maximum Likelihood Estimation technique and specific criteria for enhancing statistical modeling effectiveness. Supplementary decision guidelines involve the utilization of the determination coefficient and the p-value from the errors normality test. The algorithm's application was demonstrated using actual data. The article's conclusion highlighted the ability to identify a range of feasible solutions for selecting the optimal power parameter. However, it was acknowledging the challenge of identifying a single optimal value that satisfies the requirements of all estimation and decision methodologies.


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How to Cite

Taher Awdi , R., & Mohammed Ali, H. T. (2023). Develop a Nonlinear Regression Model Using Box-Cox Transformation with Application. Academic Journal of Nawroz University, 12(4), 306–310.